Intelligent Scheduling Method for Splitting Process Task Volumes
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Solution Overview
Problem
Current production planning and scheduling methods fail to efficiently utilize multiple machines of the same type, leading to resource waste and prolonged production times due to their inability to split task quantities across machines, resulting in missed delivery dates.
Innovation Solution
An intelligent scheduling method that sets an upper limit on parallel machines, allocates task quantities across multiple machines, and dynamically adjusts the number of parallel machines based on production needs to ensure timely completion of tasks within delivery dates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If task quantity is allocated to a single machine, then machine operation is simplified, but production efficiency decreases and delivery dates are missed
Solution Approach 1:
The patent divides the production task into multiple sub-tasks and allocates them to different machines of the same type. Each machine processes a portion of the total task quantity, enabling parallel processing and improving overall production efficiency while meeting delivery dates.
Solution Approach 2:
The patent implements dynamic task allocation where the system continuously monitors machine status, task progress, and delivery requirements. The scheduling plan is adjusted in real-time based on actual production conditions, allowing flexible redistribution of tasks to optimize efficiency and meet deadlines.
2Productivity
If multiple machines are used in parallel, then production efficiency increases, but raw material transportation cost increases
Solution Approach 1:
The patent applies partial parallelism by using multiple machines only when necessary to meet delivery dates. The system evaluates whether parallel machine usage is needed based on task urgency, machine availability, and delivery requirements, thereby balancing production efficiency gains against increased transportation costs.
Solution Approach 2:
The patent dynamically adjusts the number of parallel machines based on changing production conditions, task priorities, and delivery dates. By varying the degree of parallelism as a controllable parameter, the system optimizes the trade-off between production efficiency and transportation cost rather than using a fixed number of machines.
3Loss of time
If task quantity is not split across machines, then scheduling is simpler, but machine resources are wasted and production duration increases
Solution Approach 1:
The patent segments the production task into allocable units that can be distributed across multiple machines. This segmentation enables the system to reduce production duration by utilizing available machine resources in parallel, while the segmented structure facilitates manageable scheduling complexity through standardized allocation rules.
Data Source
AI summary
An intelligent scheduling method for supporting process task quantity splitting, which may relax the limit on the number of parallel machines for overdue task lists under the constraint of using as few parallel machines as possible, and split time-consuming process task quantities according to the operating status of machines in different periods.